Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması

Öz

Periferik kan hücrelerinin sınıflandırılması anemi ve lösemi gibi birçok kan hastalığının teşhisinde önemli rol oynamaktadır. Bu nedenle, doğru kan hücresi sınıflandırması hastalığın teşhisinde klinik olarak oldukça önemlidir. Son yıllarda, derin öğrenme, özellikle Evrişimsel sinir ağları, güçlü kendi kendine öğrenme yetenekleri sayesinde tıp alanında sıklıkla kullanılmaktadır. Bu çalışmada, kan hücre sınıflandırması için hesaplama maliyetini ve parametre sayısını azaltan derinlemesine ayrılabilir evrişim ile Inception modülünden oluşan yeni bir hibrit yöntem geliştirilmiştir. Bu yöntem, parametre sayısını ve hesaplama maliyetini azaltıp sınıflandırma doğruluğunu arttırmasıyla, standart evrişimsel sinir ağlarına göre bir avantaj sağlamaktadır. Geliştirilen yöntemin performansını test etmek için 8 sınıflı bir kan hücresi veri seti üzerinde yapılan deneysel çalışmalar sonucunda %98.89 doğruluk, %98.88 kesinlik, %98.85 duyarlılık, %98.86 F1-skoru elde edilmiştir. Literatürdeki çalışmalar ile karşılaştırıldığında yöntemimizin etkili olduğu görülmektedir.

Anahtar Kelimeler

• Periferik Kan Hücresi
• Sınıflandırma
• Inception modülü
• Derinlemesine Ayrılabilir Evrişim

Multiple Classification of Human Peripheral Blood Cells Using Modified Inception Module

Öz

Classification of peripheral blood cells plays an important role in the diagnosis of many blood diseases such as anemia, leukemia etc. Therefore, correct blood cell classification is clinically very significant in diagnosing the disease. In recent years, deep learning, especially Convolutional neural networks, has been used frequently in the medical field thanks to its strong self-learning capabilities. In this study, a new hybrid method consisting of depthwise separable convolution and Inception module has been developed, which reduces the computational cost and the number of parameters for blood cell classification. This method provides an advantage over standard convolutional neural networks by reducing the number of parameters and computational cost and increasing the classification accuracy. As a result of experimental studies conducted on an 8-class blood cell dataset to test the performance of the developed method, 98.89% accuracy, 98.88% accuracy, 98.85% sensitivity, 98.86% F1-score were obtained. It is seen that our method is effective when compared with the studies in the literature.

Anahtar Kelimeler

• Peripheral Blood Cell
• Classification
• Inception Module
• Depthwise Separable Convolution

Kaynakça

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